Today's computing clusters are more general than the M/M/\(n\)  model used in the past. In particular, the jobs typically request multiple servers simultaneously and hold onto them for the duration of the job. This difference is largely a result of machine learning jobs like TensorFlow
[1]}, [2]}, which are highly parallel. For example, when we look at
Google's Borg Scheduler [3]}, we see that the number of servers occupied by an individual job can be anywhere from 1 to 100000, as illustrated in Figure REF  [4]}, [5]}. We refer to jobs that occupy multiple servers/cores as multi-server jobs. While multi-server jobs have always existed in the niche supercomputing world, they have now become mainstream.
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